It is worth distinguishing three purposes for using synthetic data. One is to build a single network that performs well on data from any camera. It is not yet known what the limits of network performance might be; the human ability to recognize objects across many types of cameras supports the idea that a single network might be able to succeed with images from a wide range of cameras [1]}. Massive simulation, say by generating a very large number of images from many different cameras with diverse scene content, is one approach to creating such a universal network [2]}, [3]}, [4]}, [5]}. This approach synthesizes images as a data augmentation method.
